Deep Learning Invades HPC While many algorithms are commonly referred to as "machine learning" (ML) or "artificial intelligence" (AI), deep learning with neural networks (NNs) has dominated the attention of the ML industry in recent years. Though numerous alternatives exist – including support vector machines, Bayesian classifiers, genetic algorithms, clustering techniques, and even decision trees – NNs have experienced a rapid increase in real-world effectiveness during recent years. Continued improvements in computing hardware help propel the ongoing expansion in the use of NNs by many industries. In fact, the demand for larger and more-powerful neural networks motivates many to leverage the unique scaling advantages provided by high-performance computing (HPC), including Cray's high-end clusters and supercomputers. Specifically, current scaling to small node counts is no longer sufficient for today's larger NN workloads, let alone the workloads of the future.